A big group decision making problem is investigated, under the intuitionistic fuzzy environment and multiple constrains. Firstly, this paper proposes a distance formula between different intuitionistic fuzzy numbers, to measure their dissimilarity degree. A numerical example is introduced to demonstrate the advantages of the proposed distance measure over the others’ formula. Secondly, an optimizing model is constructed to calculate the criteria’s weight values, making the proposed method suitable for weight unknown problems. Thirdly, clustering idea is introduced to handle big data caused by big decision group. Here, a clustering algorithm is given which could classify the participating people. Meanwhile, computer experiments is utilized to handle the calculation question with respect to big data. Clustering result is based on many times of evolutions, which are obtained by a computer procedure. To derive final ranking results, an extended TOPSIS method is applied depending on the proposed distance measure and clustering results. In summary, a decision making algorithm is clearly shown in form of flow chart. Finally, an experimental analysis for selecting proper library construction is given to illustrate the efficiency and reasonableness of the proposed method.
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